Can Gemma 3 4B run on Intel Arc A380 6GB?

YES — With Offload

A71Great

Estimated from fit model

Gemma 3 4B needs ~6.0 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 6.0 GB, 22.2 tok/s, Runs with offload (needs ~0 GB host RAM)

6.0 GB required6.0 GB available

100% VRAM used

Fit status

Runs with offload (needs ~0 GB host RAM)

Decode

22.2 tok/s

TTFT

8731 ms

Safe context

16K

Memory

6.0 GB / 6.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.1 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsGemma 3 4B on Intel Arc A380 6GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 22.2 tok/s decode · 8.7s TTFT (warm) · 55 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	29.7 tok/s	3553 ms	16K
Coding	A	Runs with offload (needs ~0 GB host RAM)	22.2 tok/s	8731 ms	16K
Agentic Coding	F	Too heavy	11.9 tok/s	23700 ms	16K
Reasoning	A	Runs with offload (needs ~0 GB host RAM)	22.2 tok/s	10318 ms	16K
RAG	F	Too heavy	11.9 tok/s	29625 ms	16K

Quantization options

How Gemma 3 4B (4B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A75
Q3_K_S	3	2.0 GB	Low	A76
NVFP4	4	2.2 GB	Medium	A76
Q4_K_M	4	2.4 GB	Medium	A76
Q5_K_M	5	2.9 GB	High	A75
Q6_KBest for your GPU	6	3.3 GB	High	A75
Q8_0	8	4.3 GB	Very High	F0
F16	16	8.2 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 3 4B on your machine.

Run

ollama run gemma3:4b

Your hardware

More models your Intel Arc A380 6GB can run

Model	Params	Grade	Decode
Qwen 2.5 VL 7B	7B	B	14.1 tok/s
Qwen 2.5 7B	7B	B	14.1 tok/s
Codestral Mamba 7B	7B	A	16.8 tok/s
Gemma 4 E2B	5.1B	A	24.1 tok/s

Frequently asked questions

Can Intel Arc A380 6GB run Gemma 3 4B?

Yes, Intel Arc A380 6GB can run Gemma 3 4B with a A grade (Runs with offload (needs ~0 GB host RAM)). Expected decode speed: 22.2 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 6.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 4B?

The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 4B run at on Intel Arc A380 6GB?

On Intel Arc A380 6GB, Gemma 3 4B achieves approximately 22.2 tokens per second decode speed with a time-to-first-token of 8731ms using Q4_K_M quantization.

Can Intel Arc A380 6GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on Intel Arc A380 6GB receives a A grade with 22.2 tok/s and 16K context.

What context window can Gemma 3 4B use on Intel Arc A380 6GB?

On Intel Arc A380 6GB, Gemma 3 4B can safely use up to 16K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 4B feels slow on Intel Arc A380 6GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Arc A380 6GB for Gemma 3 4B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc A380 6GB See all hardware for Gemma 3 4B

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